Infrastructure

Database: is the only secure object relational data management system for multimedia-rich Internet applications, high-traffic OLTP environments, and query-intensive data warehouses. Oracle Database includes the broadest array of the most valuable services in one COTS product. It significantly enhances IT operations via automation, clustering, and high availability.

Database In-Memory: transparently accelerates data warehouse performance, enabling users to get immediate answers to queries that previously took hours and speed up OLTP processes that previously were burdened by analytics indexes, which are now no longer needed.

Oracle Data Mining sifts massive sets of data to discover hidden patterns and insights. It uses machine-learning algorithms such as anomaly detection and decision trees to predict outcomes, attribute importance and association to find relationships, and classification and clustering to group records.

Oracle R Enterprise (ORE) integrates the open source R language for statistical computing and graphics with the database. ORE extends the database’s analytical capabilities by leveraging R’s library of statistical functions and pushes down computations to the database. Users can analyze billion row data sets and leverage the database’s parallelism and scalability without learning SQL.

OLAP

OLAP extends Oracle’s analytical capability by embedding a multi-dimensional database and a high performance calculation engine. Without moving data out of the database, users analyze data along dimensions such as location and time, as well as perform what-if analyses, forecasting, and over one hundred OLAP functions.

Multitenant

Multitenant facilitates database consolidation by enabling multiple Oracle databases to share memory and processes, dramatically reducing resource requirements and management overhead. This database option leverages a container database which acts like a "database hypervisor” that hosts many pluggable databases like "database VMs" and enables rapid relocation, provisioning, and upgrade. Simplifying administration, all the pluggable databases within a container database are managed as one, while retaining the security isolation and resource prioritization of separate databases.

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Database In-Memory

Database In-Memory transparently accelerates data warehouse performance, enabling users to get immediate answers to queries that previously took hours and speed up OLTP processes that previously were burdened by analytics indexes, which are now no longer needed. It implements a dual-format architecture that simultaneously represents data in the row format for OLTP transactions and in the column format for analytic queries within the database cache. There are no additional storage costs or storage synchronization issues with In-Memory. As well, it reduces system resource consumption by compressing data in-memory 2X - 20X and eliminating analytics indexes. Moreover, it scales scale out on engineered RAC systems and transparently blends memory, flash, and disk to achieve highest performance and lowest cost.

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Flashback Data Archive

Flashback Data Archive transparently tracks the change history for any database table. Using standard SQL, users query the history of table data, as it existed in different points in time for immutable change tracking, auditing, and compliance. It uses partitioning, compression, and retention rules to efficiently reduce the storage footprint.